The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

Modelling trust in semantic web applications

This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations. The framework is established on a semantic modelling approach putting forward its suitability to resolving adoption barriers while also highlighting the characteristics of trust that can be exploited. Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation

Use-cases on evolution

This report presents a set of use cases for evolution and reactivity for data in the Web and Semantic Web. This set is organized around three different case study scenarios, each of them is related to one of the three different areas of application within Rewerse. Namely, the scenarios are: “The Rewerse Information System and Portal”, closely related to the work of A3 – Personalised Information Systems; “Organizing Travels”, that may be related to the work of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources” related to the work of A2 – Towards a Bioinformatics Web

Distributed multimodal journey planner based on mashup of individual planners’ APIs

In this research work we describe the creation of the concept of a distributed journey planning system that links as many journey planning services as are available in public transportation operators and willing to participate in one or more networks of journey planners across Europe. This is integrated on European project MASAI and it is part of a development of mobile solutions that allows journey plans in Europe based on public transportation availability, with the possibility of buying tickets in a mobile device with a multi-operator scenario. A semantic context was created in order to identify which Application-Programming Interfaces (APIs) from different public transport operators to use and set start\end trip points.info:eu-repo/semantics/acceptedVersio

Transport analysis approach based on big data and text mining analysis from social media

The goal of the study of the paper is to propose a dashboard with dynamic graphics using a qualitatively and quantitatively approach to investigate the tourists’ satisfaction according by transport mode used. The methodology implemented in the research includes data collection from TripAdvisor.com with geographic locations and their integration with statistical territorial data. Text mining techniques are applied in order to assess tourists’ perceptions on success factors, which may be used as planning support tools. The case study concerns Croatia country and shows the value and complementarity of Social Media-related data with official statistics for transport and tourism planning